Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Fitness sharing genetic algorithm is one of the most common used methods to deal with multimodal optimization problems. The algorithm requires peaks radii as the predefined parameter. It is very difficult to guess peaks radii for designers and decision makers in the real world applications. A novel self-adaptive annealing peaks radii control method has been suggested in this paper to deal with the problem. Peaks radii are coded into chromosomes and evolved while fitness sharing genetic algorithm optimizes the problem. The empirical results tested on the benchmark problems show that fitness sharing genetic algorithm with self-adaptive annealing peaks radii control method can find and maintain nearly all peaks steadily. This method is especially suitable for the problems whose peaks radii are difficult to estimate beforehand.